Analytics

created by MonaP on Jul 23, 2014 11:48 AM, last modified by DELL-SMcClure on Aug 7, 2017 6:34 AM

Version 19

Why Analytics For Big Data?

The power of Big Data has shifted the focus of analytics from measuring the past to anticipating the future. This is very much an extension of traditional data mining where you build predictive models, but the difference is that new technologies such as Hadoop provide greater agility and the ability to analyze larger, more diverse data sets. As a result, data analysts can quickly develop more accurate predictive models that deliver unprecedented value.

The EMC Federation provides technologies and solutions to meet any analytic requirement. Develop your own solution with Pivotal Big Data Suite or leverage pre-built analytic solutions such as IT Operations Analytics and Federation Security Analytics.

The solution can be deployed on a Federation Data Lake platform to extend its analytics capabilities, provide greater scalability and execute on additional big data use cases.

Gain Complete Visibility: Eliminate blind spots with visibility across logs, networks, and endpoints. Inspect every network, packet session, and log event for threat indicators at time of collection with capture time data enrichment.

Detect and Analyze:Discover attacks missed by traditional security information and event management (SIEM) and signature-based tools by correlating network packets, netflow, endpoints, and logs. Identify high-risk indicators of compromise by harnessing the power of big data and data science techniques.

Take Targeted Action:Prioritize investigations and streamline multiple analyst workflows in one tool. Instantly pivot from incidents into deep endpoint and network packet detail to understand the true nature and scope of the issue.

Through a Pivotal Big Data Suite subscription, customers store as much data as they want in fully supported Pivotal HD, paying for only value added services per core – Pivotal Greenplum Database, GemFire, SQLFire, GemFire XD, and HAWQ. The significance of this new consumption model is that customers can now store as much Big Data as they want, but only be charged for the value they extract from Big Data.